platform · Microsoft
AI Infrastructure & MLOps with Azure OpenAI
AI Infrastructure & MLOps built on Azure OpenAI, chosen where it genuinely fits, and swapped where it does not.
- Category
- platform
- Vendor
- Microsoft
- Alternatives we also use
- 7
Why Azure OpenAI for this
Most GPU spend is oversizing. We measure your real throughput and latency requirements first, and the answer is often smaller and cheaper than expected.
Azure OpenAI is strongest at enterprise compliance posture and integration with existing Microsoft estates. For ai infrastructure & mlops that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: quota management and regional capacity can constrain scaling at short notice. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong.
You own the code, the models where they are open-weight, and the documentation to run it without us.
The honest assessment
- What it is
- OpenAI models under Azure's compliance envelope and enterprise agreements.
- Strongest at
- enterprise compliance posture and integration with existing Microsoft estates
- Trade-off
- quota management and regional capacity can constrain scaling at short notice
- Category
- platform
We are not a reseller for Microsoft and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Workload sizing based on measured throughput, not guesses
- Model registry and versioned deployments
- Autoscaling and cost-per-inference monitoring
- Canary and rollback deployment paths
- On-premise or air-gapped options where required
- Runbooks and on-call documentation
Questions
Cloud or on-premise?
We model both against your real volume. On-premise typically wins at sustained high throughput or where data residency is non-negotiable; cloud wins on variable and early-stage workloads.
Can you deploy air-gapped?
Yes, with open-weight models and a fully offline inference stack, the usual pattern for defence, and for some healthcare and government work.
Do you support our existing Kubernetes setup?
Yes, and we would rather extend it than introduce a parallel platform your team has to learn.
Alternatives for ai infrastructure & mlops
Same capability, different stack. Each page states its own trade-off.
Building with Azure OpenAI?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878